Her optimal learning is given by the concavificationThis approach is famously used by aumann1995repeated to characterize the limiting value of repeated games with one-sided incomplete information. [1]} provide a recent promulgation of this technique. of this function and is pinned down by the two posterior beliefs \(u_{L}\)  and \(u_{H}\) . Explicitly,
\(u_{L} = a + p_{i} - \frac{1}{4 \kappa }, \quad \text{and} \quad u_{H} = a + p_{i} + \frac{1}{4 \kappa }, \quad \text{where} \quad \mathbb {P}\left(u_{H}\right) = \frac{1}{2} + 2\kappa \left(\mu - p_{i}-a\right)\qquad \mathrm {(1)}\) 
